The safe and effective practice of medicine relies greatly upon a physician's obtaining accurate and complete medical records for a patient who is to undergo treatment. Typically, medical records consist of multiple paper forms, physician's notes, test and diagnostic imaging results, and the like. While current medical recordkeeping maintains an effective index to a patient's condition, a patient seeing a new physician, whether a specialist or a new primary care physician, must ensure that his/her medical history, including all past medical diagnoses, is immediately and understandably accessible to the physician. This often requires the transfer of volumes of paper back-and-forth between various medical offices with all of the attendant disadvantages associated with copying, filing and review.
Adoption of electronic medical recordkeeping has not occurred with the same pace and enthusiasm attendant to the adoption of other forms of electronic recordkeeping. Transition performance has been poor for a multitude of reasons, chief of which is the difficulty associated with converting existing medical and patient information to a convenient electronic format.
For example, the most commonly used database (catalog) of medical diagnoses by physicians is termed the International Classification of Disease, 9th th Ed. (ICD9) which consists of the detailed list of medical diagnosis which have been extensively compiled over time. The ICD9 is viewed as the gold standard and since it is expressed in a lexicography design for physician use, it generally possesses the descriptive medical rigor necessary for efficient patient management and electronic recordkeeping. Further, it is extensively used as a standard for disease and treatment coding which is necessary for billing in most areas of the United States, particularly in the context of insurance claim processing. However, the ICD9 comprises an estimated 15,000 ICD9 disease codes and searching through them (even electronically) for an appropriate diagnosis code is a tedious and time consuming task for any physician. Additionally, and even more important, there are many conceptually similar ICD9 disease codes which contain no hierarchical links or ontological association to one another, making searches in accordance with disease category or classification inefficient and laborious, if not impracticable.
For example, the disease terms fetal polyhydramnios, intrauterine growth retardation, intrapartum hemorrhage, hyperemesis gravidarum, erythroblastosis fetalis, and hemolytic disease of the newborn all imply that the patient is pregnant, yet none of them contain or refer to the word “pregnancy.” Moreover, some are not even classified as diseases of pregnancy. For example, in many ontologies, a hydatiform mole is classified as a malignancy, rather than a disease of pregnancy. Thus, performing a computerized search using an electronic search string including the term “pre*” would fail to return any or all of these diagnoses, depending upon how they were initially categorized and the ontology in which they were categorized. Even when the physician knows the exact disease term desired, computer-based searching can still be inconvenient and inefficient. For example, many medical diseases such as Klippel Feil Syndrome have a non-intuitive spelling, making searching an often frustrating experience. It should be noted that the above limitations are not specific to the ICD9 cataloging system, but to any catalog of medical diseases which might exist (e.g., SnoMed, created by American College of Pathologists).
A typical elderly patient may be taking five different medications, have certain conditions that correspond to five different medical diagnosis, and exhibit an allergic reaction to two different forms of medications. Moreover, a typical physician will very likely have approximately 1,500, or more, patients registered in his/her office practice. Accordingly, this typical individual physician may require electronic data entry for approximately 18,000 data points in order to collect and compile the most basic information about their patients (15,000 patients, each taking five medications on average, and each having five prior medical diagnoses and two allergies, on average). For a typical physician practice group of five or fewer doctors, the cost of manual data entry and upkeep can be financially prohibitive, as well as very demanding of a physician's time, since many physicians would not entrust a non-physician to encode a patient's past medical diagnoses, due to the consequence of inaccurate entry.
While a lay person can easily list their current medications and allergies, e.g., by reading and transcribing a prescription label, very few are able to articulate their specific medical diagnoses. Typically, a patient understands primarily common lay terms which describe the diagnosis he/she may have. For example, a patient may inform the physician that he/she has “an irregular heartbeat” or “heart flutter” but is not able to articulate the accurate medical diagnosis of an “accessory intranodal bypass tract associated with rapid ventricular response”. Necessarily, however, an accurate medical diagnosis using rigorous medical terminology is crucial for optimal patient management, as well as computerized medical decision support. Hence, electronic medical records which are to be utilized by physicians ideally necessitate accurate diagnoses using specific and detailed medical terminology such as that found in the ICD9 vocabulary.
Accordingly, there is a strong need for both systems and methods by which information relating to any particular patient's current medications, conditions, allergies, and the like, to be rapidly entered into an electronic database with minimal cost and minimal time commitment on the part of a physician. Such systems and methods should be simple, cost-effective and allow for participation and/or use by lay persons such as office clerical staff and a patient themselves.